Extraction Of Mineral Resources Research Articles (Page 1)

Accurate prediction of mean fragment size is a fundamental requirement for enhancing operational efficiency, reducing ecological disturbances, and fostering the sustainable use of mineral resources. However, traditional empirical and statistical approaches often struggle with high-dimensional variables, limited computational speed, and the challenge of modeling small or sparse datasets. This study proposes a hybrid machine learning optimization framework that integrates Random Forest (RF), Whale Optimization Algorithm (WOA), and Extreme Gradient Boosting (XGBoost). Based on high-dimensional and small-sample data collected from historical blasting operations in open-pit mines, the framework employs a data-driven approach to construct a prediction model for mean fragment size, with the aim of enhancing the sustainability of mineral resource extraction through optimized blast design. The raw blasting fragmentation dataset was first preprocessed using a multi-step procedure to improve data quality. RF was then employed to assess and select 19 input features for dimensionality reduction, while WOA was utilized to optimize the hyperparameters of the predictive model. Finally, XGBoost was applied to model the small-sample blasting fragmentation dataset. Comparative experiments demonstrated that the proposed model achieved superior predictive performance with a coefficient of determination (R2) of 0.93. In addition, the cosine amplitude method was used to analyze the sensitivity of different variables affecting the mean fragment size (MFS), and the SHAP method was applied to quantitatively reveal the marginal contribution of each input variable to the prediction.

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities.

The article examines the ecological characteristics of the landscapes along the Pacific Railway route in the Mulam River basin. This project is highly relevant for transportation and logistics infrastructuresupporting mineral resource extraction, particularly for increasing exports of high-quality coal from the Elga deposit in South Yakutia to East and Southeast Asia. The railway traverses a remote, uninhabited area with insufficientlystudied permafrost landscapes. The development of this territory entails environmental risks due to the low resilience of permafrost geosystems to anthropogenic disturbances. The study revealed that the researched geosystems have degrees of stability, ranging from high to relatively low–depending on their natural structure. Additionally, an assessment of anthropogenic transformation was conducted, accounting for differences in the nature and intensity of economic activity, with areas classified by their degree of transformation (high, relatively high, and relatively low). A landscape (geosystem) zoning map of the territory adjacent to the Pacific Railway was developed, categorizing natural complexes based on their stability and anthropogenic transformation.

The aim of this study is to analyze the representation of geological, mining, processing, and environmental processes in platform Minecraft. Based on a methodological comparison of in-platform mechanics with technological and geoscientific procedures, the article assesses the degree of accuracy, simplification, and didactic applicability of individual processes related to the extraction and use of mineral resources. The analysis is structured into seven main thematic areas covering the entire resource value chain—from geological exploration through mining, ore beneficiation and processing, to quantitative indicators (e.g., waste-to-ore ratio), fluid resources, and environmental impacts. Special attention is given to the potential of modifications that significantly enhance the complexity and accuracy of simulated processes. The results show that Minecraft, enriched with thematic mods, can serve as an accessible and flexible tool for the popularization and education of industrial and geoscientific processes, while engaging a wide range of audiences.

The global accumulation of phosphogypsum (PG), with annual generation exceeding 175 Mt/year, presents significant environmental challenges. While studies have demonstrated PG's potential as a filler material in geopolymer composite, comprehensive environmental impact assessments of such valorization approaches remain limited. This study presents the first comparative life cycle assessment (LCA) of acid- and alkali-activated PG-CFA geopolymers in the context of sustainable industrial waste management. Geopolymer technology can eliminate the need for traditional landfilling of PG in coastal areas and, therefore, reduce their negative environmental impacts. LCA was conducted to assess the impacts of repurposing 1kg functional unit of PG as geopolymer precursors coupled with acid- and alkali-based activators compared to the current disposal practices of these solid wastes. The inventory was modeled after a phosphoric acid plant using the wet dihydrate process, a coal-fired power plant, and a laboratory-scale coal fly ash-phosphogypsum geopolymer (FAPG) synthesis upscaled for industrial application. The most number of environmental benefits was observed for acid FAPG particularly via reductions in CO2-eq emissions by 40%, 90% in energy consumption, and 36% in mineral resource extraction. Alkali FAPG excelled in water acidification and scarcity by 60% and it could outperform acid FAPG environmentally via sensitivity analysis under a similar formulation blend. Further research can focus on optimizing FAPG formulation, finding alternatives for the acid and alkali activators, and reviewing industrial standards for widespread FAPG applications. These results imply the potential of integrating FAPG manufacturing in PG- and CFA-generating industries to emulate a circular economy.

As mineral resource extraction deepens, large and medium-sized metal mines increasingly utilize chutes for ore transportation. However, the impact airflow and dust generated by high chutes significantly contaminate the working environment. To investigate the linear and nonlinear relationships between the maximum impact wind speed, the average dust concentration, and various factors, this study employs similar simulation experiments. By modifying conditions such as ore discharge height, discharge flow rate, ore particle size, ore moisture content, and chute resistance, the impact airflow and dust concentration at the discharge port were monitored. Furthermore, a Grey Relational Analysis (GRA) was conducted on the five main factors. The results indicate that the maximum impact wind speed and the average dust concentration positively correlate with discharge flow rate and discharge height, and negatively correlate with ore particle size range and chute resistance coefficient. As moisture content increases, dust concentration decreases rapidly; however, once moisture content exceeds 3.5%, the decreasing trend becomes less pronounced. Among the factors affecting impact wind speed, discharge height and chute resistance coefficient play a dominant role, followed by discharge flow rate and ore particle size, while ore moisture content has a relatively minor influence. Regarding key factors influencing dust concentration, ore moisture content and discharge height are most significant, followed by chute resistance coefficient, discharge flow rate, and ore particle size. This study provides a theoretical foundation for targeted dust control measures in high chute ore discharging.

Abstract An abundance of natural resources does not always translate into economic growth. Rather, it can be detrimental to human capital, which is crucial for long-term economic development. Using panel data from 2000 to 2019 for 14 provinces (oblasts) and two major cities in Kazakhstan, this paper provides empirical evidence of a resource curse on human capital development. Regions that rely heavily on mineral resources spend very little on education. Mineral resource extraction, and oil and gas extraction in particular, is negatively associated with expenditure on education.

ABSTRACT In recent years, apparent supply crises and geopolitical pressures around the globe have injected new demands for the extraction, processing and recycling of mineral resources. Particularly in countries of the Global North, the temporal pressure to combat climate change and to ensure energy security became increasingly entangled with political calls for securing ‘critical minerals’ like lithium domestically. The paper explores how particular tropes of ‘mining for climate’ build upon the infrastructures and residues of prior extractive operations in the German Ore Mountains. Based on ethnographic research within a transdisciplinary remining project, we identify material and discursive elements linking conventional with ‘green’ forms of extractivism(s). In the context of new mining legislations such as the EU’s Critical Raw Materials Act (CRMA), local struggles around corporate and state-led attempts of speeding up permitting procedures and of reusing extractive infrastructures point to specific lithium crystallizations where alternative forms of future-making take shape.

Subject. The article discusses consolidated and federal budgets of Russia for 2017–2024. Objectives. The purpose is to establish the current state of affairs in the budget system and determine the relevance of the current tax policy in relation to oil and gas revenues in the context of economic sanctions. Methods. The study employs methods of comparative analysis and generalization of budget system documents. Results. The paper established the need to ensure reliable forecasting within the entire budget system, where oil and gas revenues still play an important role in ensuring balance, to review the main taxation instruments of the oil and gas industry for the development of oil refining and petrochemical. It determined that the price of Urals crude oil is one of the key instruments for generating income and ensuring the balance of the entire budget system. Furthermore, the tax maneuver has not yet contributed to a noticeable increase in the depth of oil refining in the country and represents a rather limited mechanism of manual control, while the rules for calculating taxes on the extraction of mineral resources and additional income from the extraction of hydrocarbon raw materials are quite complex. The price of Urals crude oil and the ruble exchange rate do not fully depend on government actions, replenishment of the National Welfare Fund is difficult, access to external sources of financing the country's deficit is limited, and raising funds through domestic loans seems to be quite expensive. Conclusions. Reliable forecasting at the level of the entire budget system and reduction of oil and gas dependence are of particular importance. It is necessary to adjust the articles of the Tax Code of the Russian Federation and the Law of the Russian Federation "On Customs Tariff" related to oil and gas revenues.

To ensure the green, safe, and efficient extraction of mineral resources and promote sustainability, the stability of mined-out areas has become a critical factor affecting safe production and ecological restoration in underground metal mines. The instability of underground goafs poses a significant threat to mine safety, especially when irregular excavation patterns interact with high ground stress, exacerbating instability risks. Most existing studies lack a systematic and multidisciplinary integrated framework for comprehensive evaluation and management. This paper proposes a trinity research system of “assessment–optimization–governance”, integrating theoretical analysis, three-dimensional fluid–solid coupling numerical simulation, and a filling sequence optimization method based on genetic algorithms. An analysis of data measured from 243 pillars and 49 goafs indicates that approximately 20–30% of the pillars have a factor of safety (FoS) below 1.0, signaling immediate instability risks; additionally, 58% do not meet the threshold for long-term stability (FoS ≥ 1.5). Statistical and spatial analyses highlight that pillar width-to-height ratio (W/H) and cross-sectional area significantly influence stability; when W/H exceeds 1.5, FoS typically surpasses 2.0. Numerical simulations reveal pore water pressures of 1.4–1.8 MPa in deeper goafs, substantially reducing effective stress and accelerating plastic zone expansion. Stability classification categorizes the 49 goafs into 7 “poor”, 37 “moderate”, and 5 “good” zones. A genetic algorithm-optimized filling sequence prioritizes high-risk area remediation, reducing maximum principal stress by 60.96% and pore pressure by 28.6%. Cemented waste rock filling applied in high-risk areas, complemented by general waste rock filling in moderate-risk areas, significantly enhances overall stability. This integrated method provides a scientific foundation for stability assessment and dynamic remediation planning under complex hydrogeological conditions, offering a risk-informed and scenario-specific application of existing tools that improves engineering applicability.

Subject. This article discusses the issues of efficiency of mineral resource extraction and the integration of the economy of the Donetsk People's Republic into the economy of Russia. Objectives. The article aims to analyze the prospects for the development of the coal industry, and develop risk management tools characteristic of the fuel and energy complex as a whole. Methods. For the study, we used the general scientific research methods. Results. The article proposes a classification of risks characteristic of enterprises in the fuel and energy complex. It presents a risk management system facilitating the acquisition of information about all potential threats. The article finds that the requirements of the Russian and international standards are applicable to the enterprises of the fuel and energy complex of the Donetsk region. Conclusions. The results of the study can be taken into account at the national and regional levels when designing programmes for the development of the coal industry and fuel and energy complex.

Real-time mining (RTM) has become increasingly significant in response to the growing need for sustainable mineral resource extraction, driven by global population growth and technological progress. This innovative approach addresses critical challenges, such as declining ore grades, deeper and less accessible deposits, and rising energy costs, by integrating advanced online grade monitoring, data analysis, and process optimization. By employing real-time grade control, dynamic mine planning, and production optimization, it enhances the efficiency of resource extraction while minimizing environmental and social impacts. Originally proposed about a decade ago, RTM has gained attention for its potential to revolutionize the industry. This review examines recent advancements in closed-loop concepts, emphasizing the integration of advanced sensors and data analytics to enable continuous monitoring and adaptive decision making across the mining value chain. It highlights the role of online sensor technologies in providing high-resolution data for process optimization and evaluates various mining optimization techniques. The paper also explores data assimilation methods, such as Kalman filters and artificial intelligence (AI), showcasing their ability to continuously update models and reduce operational uncertainties. Ultimately, it proposes a comprehensive framework for adaptive, data-driven mining operations that promote sustainable development, enhance profitability, and improve decision-making capabilities.

The article is devoted to the study of ways to expand the turnover of derivative financial instruments in Armenia. The purpose of the study is to explore the possibilities of using derivative financial instruments in the Republic of Armenia based on the results of the analysis. The paper examined the legislative and legal bases of derivative financial instruments in Armenia, considered ways to expand their circulation, and discussed the importance of derivative financial instruments in the organization's financial management system. As a result of the analysis, the possibilities of using derivative financial instruments by companies operating in the field of extraction of mineral resources of the Republic of Armenia were assessed.

As the demand for deep mineral resource extraction intensifies, optimizing pipeline transportation systems in backfill mining has become increasingly critical. Thus, reducing energy loss while ensuring the filling effect becomes crucial for improving process efficiency. Owing to variations among mines, accurately calculating pipeline resistance loss remains challenging, resulting in significant inaccuracies. The rapid development of Industry 4.0 provides intelligent and data-driven optimization ideas for this challenge. This study introduces a novel pipeline resistance loss prediction framework integrating generative artificial intelligence with a TransKAN model. This study employs generative artificial intelligence to produce physically constrained augmented data, integrates the KAN network’s B-spline basis functions for nonlinear feature extraction, and incorporates the Transformer architecture to capture spatio-temporal correlations in pipeline pressure sequences, enabling precise resistance loss calculation. The experimental data collected from pipeline pressure sensors provides empirical validation for the model. Compared with traditional mathematical formulas, BP neural networks, SVMs, and random forests, the proposed model demonstrates superior performance, achieving an R2 value of 0.9644, an RMSE of 0.7126, and an MAE of 0.4703.

As the increasing demand for deep mineral resource extraction and the construction of deep vertical shafts by the artificial ground freezing method, the stability and safety of shaft that traverse thick alluvial depend significantly on their interaction with the surrounding deep frozen soil medium. Such interaction is directly conditioned by the mechanical properties of the deep frozen soil. To precisely capture these in-situ mechanical properties, the mechanical parameters tests using remodeled frozen specimens cannot ignore the disparities in consolidation history, stress environment and formation conditions between the deep and shallow soils. This study performs a series of “long-term high-pressure K0 consolidation (where K0 represents the static earth pressure coefficient, describing the ratio of horizontal to vertical stress under zero lateral strain conditions)”, “freezing under sustained load” and “unloading triaxial shear” tests utilizing remodeled deep clay. This study presents the response of unloading strength and damage properties under varying consolidation stresses, durations, and freezing temperatures. The unloading strength increases sharply and then stabilizes with consolidation time. The unloading strength shows an approximate linear positive correlation with the consolidation stress, while a negative correlation with the freezing temperature. The strengthening rate of the unloading strength due to freezing temperature tends to decrease with increasing consolidation time. Additionally, an improved damage constitutive model was proposed and validated by incorporating the initial K0 stress state and a Weibull-based assumption for damage elements. Based on the back propagation (BP) neural network, a prediction method for the stress–strain curve was offered according to the consolidation stress level, initial stress state, and temperature. These results can provide references for improving the mechanical testing methods of deep frozen clay and revealing differences in mechanical properties between deep and shallow soils.

Based on MODIS remote sensing images and NDVI values, this research systematically investigates the temporal-spatial variations of vegetation coverage and their underlying drivers in the Qinshui coal mining area over a 20-year period (2001–2020).This study investigates vegetation dynamics in mining-affected ecosystems, with three primary objectives: to characterize vegetation cover evolution patterns under mineral resource extraction pressures;to quantify natural factors influencing vegetation changes; and to establish an empirical foundation for ecological rehabilitation strategies. The key findings reveal:(1) Spatial-temporal analysis demonstrates marked vegetation improvement across the Qinshui mining region (2000-2020), with distinct geographic variability - western and southern sectors exhibit superior vegetation conditions compared to central areas.(2) Statistical modeling identifies precipitation as the dominant climatic driver, showing strong positive correlation with NDVI, while temperature exhibits weaker association. Temperature generally has a positive correlation with vegetation index values, but its spatial influence varies significantly across regions. (3) Human activities have significantly influenced vegetation cover changes. The most intensive development of industrial, mining and residential zones is concentrated in northeastern and southeastern sectors, particularly near urban agglomerations. This urban expansion has directly reduced both grassland and farmland coverage. Grassland has largely been converted to cultivated land and forest, whereas forest loss is most evident in Qin County, Anze County, Qinshui County, and Fushan County.

This paper addresses the problem of infill drill hole placement for mineral resource estimation and classification. The placement is considered optimal when it maximizes an objective function that accounts for ore grades, mineral resource classes, extraction priorities, and block volumes, where the grade and resource classes are defined on the basis of a set of geostatistical simulations. To expedite the identification of the optimal solution within a condensed timeframe, modifications to the random search (RS) algorithm are introduced, including a partition of the region targeted for drilling and the definition of a maximum distance to existing drill holes. The modified RS divides the study area into smaller areas and examines all these areas to find the optimal solution, in order to reduce the search time and to reach the best possible solution. This approach, furthermore, eliminates the impact of different random starting points and the risk of getting trapped in certain areas of the solution space. Also, the incorporation of a geometallurgical parameter (recovered metal) instead of the ore grade represents an innovation that signifies the consideration of mineral processing perspectives to optimize the drill hole placement. The proposed modified RS algorithm is applied to a dataset from an Iranian iron deposit consisting of 240 exploration drill holes, and resulted in 11% to 21% of the indicated resources being converted into measured resources after locating nine infill drill holes accounting for the iron grade and the recovered metal, respectively. The modified RS also compares favorably to other traditional optimization techniques.

Abstract This paper explores the global landscape of civil resistance to mineral extraction and its implications for the political sustainability of the energy transition. As global demand for critical minerals accelerates in association with the energy transition, there is a growing imperative to secure mineral access while improving environmental and social outcomes. However, mining activities face significant resistance worldwide, posing major challenges to the justice and viability of the energy transition. Using an original dataset derived from the GDELT Project between 2015 and 2022, we provide the first systematic global mapping of conflict and cooperation in mining regions, spanning diverse socio-political contexts and offering novel insights into the economic, environmental, and justice-related drivers of these dynamics. Our findings reveal that resistance to mineral extraction is not confined to poorer, emerging economies but is instead widespread, occurring wherever mineral deposits are found, regardless of a country’s income level. This resistance frequently reaches high levels of polarisation, often leading to costly delays and project cancellations. Although cooperation sometimes accompanies conflict, high-commitment cooperative actions are limited and less frequent in highly polarised situations. These insights highlight the need to move beyond traditional Corporate Social Responsibility approaches and existing public participation efforts within Environmental Impact Assessments. A just, sustainable, and democratic transition requires a deeper democratisation of investment decisions through inclusive governance frameworks that tackle the several injustices associated with mineral resource extraction.

As mineral resource extraction progresses to greater depths, it has become imperative for geomechanical applications to understand the thermomechanical degradation mechanisms of rocks under thermal loading. To investigate the thermomechanical characteristics of granite subjected to thermal treatments ranging from ambient to 1000 °C, we conducted uniaxial compression tests integrating P-wave velocity measurements, digital image correlation (DIC), and acoustic emission (AE) monitoring. The key findings reveal the following: (1) the specimen volume exhibits thermal expansion while the mass loss and P-wave velocity reduction demonstrate a temperature dependence; (2) the uniaxial compressive strength (UCS) and elastic modulus display progressive thermal degradation, while the peak strain shows an inverse relationship with temperature; (3) acoustic emission signals exhibit a strong correlation with failure–time curves, progressing through three distinct phases: quiescent, progressive accumulation, and accelerated failure, and fracture mechanisms transition progressively from tensile-dominated brittle failure to shear-induced ductile failure with increasing thermal loading; and (4) the damage evolution parameter exhibits exponential growth beyond 600 °C, reaching 98.85% at 1000 °C, where specimens demonstrate a complete loss of load-bearing capacity. These findings provide critical insights for designing deep geological engineering systems involving thermomechanical rock interactions.

Solar-driven interfacial water evaporation (SIWE) can efficiently utilize solar energy to separate or extract various ions from saline water, providing an environmentally friendly, economical, and sustainable approach to clean water and critical mineral resources harvesting. However, for ongoing practical implementation, solid salt accumulation at the interface will inevitably impair the SIWE performance, while direct disposal of residual concentrated brine poses significant environmental risks. As such, advancing solar evaporators for sustainable clean water harvesting and critical mineral resources extraction is pivotal in the resources-energy-environment nexus. Critically, this review spotlights the latest research progress in engineering nonselective salt-rejecting solar evaporators (NS-SRSEs) for sustainable desalination, emphasizing interfacial structural design and surface modification. We then delineate our endeavors aimed at the construction strategies of selective salt-extraction solar evaporators (S-SESEs) for getting access to critical mineral resources such as uranium and lithium. Finally, current challenges and opportunities are outlined in the high-value saline water utilization of NS-SRSE and S-SESE for real-world applications that balance high efficiency, durability, and adaptability with a low environmental impact. Looking ahead, we anticipate ongoing advancements in promoting solar evaporators from laboratory research to practical applications, contributing to global efforts in sustainable water management and critical mineral resources recovery.